Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tranhieutt/software_development_department --skill reverse-documentgit clone --depth 1 https://github.com/tranhieutt/software_development_departmentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tranhieutt/software_development_department/reverse-document)<a href="https://agentmods.dev/skills/tranhieutt/software_development_department/reverse-document"><img src="https://agentmods.dev/badge/skills/tranhieutt/software_development_department/reverse-document/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tranhieutt/software_development_department/reverse-document"><img src="https://agentmods.dev/badge/skills/tranhieutt/software_development_department/reverse-document.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.02269 |
| Opus 5 | $0.00020 | $0.01135 |
| Sonnet 5 | $0.00008 | $0.00454 |
| Haiku 4.5 | $0.00004 | $0.00227 |
Grade A, and why
reverse-document scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reverse Documentation
This skill analyzes existing implementation (code, prototypes, systems) and generates appropriate design or architecture documentation. Use this when:
- You built a feature without writing a design doc first
- You inherited a codebase without documentation
- You prototyped a mechanic and need to formalize it
- You need to document "why" behind existing code
Workflow
1. Parse Arguments
Format: /reverse-document <type> <path>
Type options:
design→ Generate a product design document (PRD section)architecture→ Generate an Architecture Decision Record (ADR)concept→ Generate a concept document from prototype
Path: Directory or file to analyze
src/api/auth/→ All combat-related codesrc/core/event-system.cpp→ Specific fileprototypes/stealth-mech/→ Prototype directory
Examples:
/reverse-document design src/api/payment
/reverse-document architecture src/core/entity-component
/reverse-document concept prototypes/vehicle-combat
2. Analyze Implementation
Read and understand the code/prototype:
For design docs (PRD):
- Identify mechanics, rules, formulas
- Extract business logic values (damage, cooldowns, ranges)
- Find state machines, ability systems, progression
- Detect edge cases handled in code
- Map dependencies (what systems interact?)
For architecture docs (ADR):
- Identify patterns (ECS, singleton, observer, etc.)
- Understand technical decisions (threading, serialization, etc.)
- Map dependencies and coupling
- Assess performance characteristics
- Find constraints and trade-offs
For concept docs (prototype analysis):
- Identify core mechanic
- Extract emergent business patterns
- Note what worked vs what didn't
- Find technical feasibility insights
- Document user fantasy / feel
3. Ask Clarifying Questions (Collaborative Protocol)
DO NOT just describe the code. ASK about intent:
Design questions:
- "I see a stamina system that depletes during combat. Was this for:
- Pacing (prevent spam)?
- Resource management (strategic depth)?
- Or something else?"
- "The stagger mechanic seems central. Is this a core pillar, or supporting feature?"
- "Damage scales exponentially with level. Intentional power fantasy, or needs rebalancing?"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 284 lines · 40 tokens per session scan A 7502f313186f
reverse-document is a skill published in the GitHub repository tranhieutt/software_development_department (72 stars, last pushed 4mo ago), licensed MIT. It adds 40 tokens to every session and 2,269 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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